HuSc3D: Human Sculpture Dataset for 3D Object Reconstruction
Abstract 3D scene reconstruction from 2D images is important tasks in computer graphics. Unfortunately, existing datasets and benchmarks concentrate on idealized synthetic or meticulously captured realistic data. Such benchmarks fail to convey the inherent complexities encountered in newly acquired real-world scenes. In such scenes the background is often dynamic, and by popular usage of cell phone cameras, there might be discrepancies in, e.g., white balance. To address this gap, we present HuSc3D, a novel dataset specifically designed for rigorous benchmarking of 3D reconstruction models under realistic acquisition challenges. Our dataset features six highly detailed, fully white sculptures characterized by intricate perforations and minimal textural variation. Furthermore, the number of images per scene varies significantly, introducing the additional challenge of limited training data for some instances alongside scenes with a standard number of views. By evaluating 3D reconstruction methods on this diverse dataset, we demonstrate the distinctiveness of HuSc3D in effectively differentiating model performance, particularly highlighting the sensitivity of methods to fine geometric details, color ambiguity, and varying data availability – limitations often masked by more conventional datasets.
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PR-ENDO: Physically Based Relightable Gaussian Splatting for Endoscopy
Abstract Endoluminal endoscopic imaging—used in diagnosing colorectal cancer and other internal diseases—stands to benefit greatly from accurate 3D reconstructions and novel-view synthesis. However, current methods struggle with artifacts due to constrained camera trajectories and lighting effects that depend heavily on view, often overfitting and failing when viewing from novel angles.
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